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34
Models for Longitudinal Network Data
 Models and Methods in Social Network Analysis
, 2005
"... This chapter treats statistical methods for network evolution. It is argued that it is most fruitful to consider models where network evolution is represented as the result of many (usually nonobserved) small changes occurring between the consecutively observed networks. Accordingly, the focus is o ..."
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Cited by 70 (8 self)
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This chapter treats statistical methods for network evolution. It is argued that it is most fruitful to consider models where network evolution is represented as the result of many (usually nonobserved) small changes occurring between the consecutively observed networks. Accordingly, the focus is on models where a continuoustime network evolution is assumed although the observations are made at discrete time points (two or more). Three models are considered in detail, all based on the assumption that the observed networks are outcomes of a Markov process evolving in continuous time. The independent arcs model is a trivial baseline model. The reciprocity model expresses effects of reciprocity, but lacks other structural effects. The actororiented model is based on a model of actors changing their outgoing ties as a consequence of myopic stochastic optimization of an objective function. This framework offers the flexibility to represent a variety of network effects. An estimation algorithm is treated, based on a Markov chain Monte Carlo implementation of the method of moments.
Modeling the coevolution of networks and behavior
 In
, 2006
"... A deeper understanding of the relation between individual behavior and individual actions on one hand and the embeddedness of individuals in social structures on the other hand can be obtained by empirically studying the dynamics of individual outcomes and network structure, and how these mutually a ..."
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Cited by 68 (12 self)
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A deeper understanding of the relation between individual behavior and individual actions on one hand and the embeddedness of individuals in social structures on the other hand can be obtained by empirically studying the dynamics of individual outcomes and network structure, and how these mutually affect each other. In methodological terms, this means that behavior of individuals – indicators of performance and success, attitudes and other cognitions, behavioral tendencies – and the ties between them are studied as a social process evolving over time, where behavior and network ties mutually influence each other. We propose a statistical methodology for this type of investigation and illustrate it by an example.
Statistical analysis of longitudinal network data with changing composition
, 2003
"... Markov chains can be used for the modeling of complex longitudinal network data. One class of probability models to model the evolution of social networks are stochastic actororiented models for network change proposed by Snijders. These models are continuoustime Markov chain models that are imple ..."
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Cited by 52 (10 self)
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Markov chains can be used for the modeling of complex longitudinal network data. One class of probability models to model the evolution of social networks are stochastic actororiented models for network change proposed by Snijders. These models are continuoustime Markov chain models that are implemented as simulation models. The authors propose an extension of the simulation algorithm of stochastic actororiented models to include networks of changing composition. In empirical research, the composition of networks may change due to actors joining or leaving the network at some point in time. The composition changes are modeled as exogenous events that occur at given time points and are implemented in the simulation algorithm. The estimation of the network effects, as well as the effects of actor and dyadic attributes that influence the evolution of the network, is based on the simulation of Markov chains.
A Multilevel Network Study of the Effects of Delinquent Behavior on Friendship
 Journal of Mathematical Sociology
, 2003
"... A multilevel approach is proposed to the study of the evolution of multiple networks. In this approach, the basic evolution process is assumed to be the same, while parameter values may di#er between different networks. For the network evolution process, stochastic actororiented models are used, ..."
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Cited by 41 (8 self)
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A multilevel approach is proposed to the study of the evolution of multiple networks. In this approach, the basic evolution process is assumed to be the same, while parameter values may di#er between different networks. For the network evolution process, stochastic actororiented models are used, of which the parameters are estimated by Markov chain Monte Carlo methods.
Imputation of missing network data: some simple procedures
 JOURNAL OF SOCIAL STRUCTURE
, 2009
"... Analysis of social network data is often hampered by nonresponse and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ..."
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Cited by 24 (0 self)
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Analysis of social network data is often hampered by nonresponse and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: modelbased methods within the framework of exponential random graph models, and imputation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for nonresponse in a few specific situations.
The evolution of intraorganizational trust networks
 International sociology
, 2005
"... The online version of this article can be found at: ..."
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Cited by 12 (2 self)
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The online version of this article can be found at:
An actororiented dynamic network approach: the case of interorganizational network evolution, Organizational Research Methods 10
, 2007
"... There is a growing interest in intra and interorganizational network dynamics. The central question in the latter domain is, ‘‘How do firms choose collaborative partners given their present network configuration, their goals, and characteristics, to get a strategic network position?’ ’ We introdu ..."
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Cited by 10 (1 self)
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There is a growing interest in intra and interorganizational network dynamics. The central question in the latter domain is, ‘‘How do firms choose collaborative partners given their present network configuration, their goals, and characteristics, to get a strategic network position?’ ’ We introduce actororiented network models as a method to describe and explain the development of interorganizational collaboration networks. The models are applied to longitudinal data about collaborative agreements within the genomics industry.
Statistical methods for studying the evolution of networks and behavior
, 2007
"... ter verkrijging van het doctoraat in de ..."
ADOLESCENT AGGRESSIVE BEHAVIOR Status and Stimulation Goals in Relation to the Peer Context
"... Cover photograph: ‘It’s in the air ’ by Polina Sergeeva 3 RIJKSUNIVERSITEIT GRONINGEN ..."
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Cited by 3 (0 self)
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Cover photograph: ‘It’s in the air ’ by Polina Sergeeva 3 RIJKSUNIVERSITEIT GRONINGEN